Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods.

ABSTRACT: We compare multiple machine learning algorithms and develop models to predict future hospitalization among Home- and Community-Based Services (HCBS) Users. Furthermore, we calculate feature importance, the score of input variables based on their importance to predict the outcome, to identify the most relevant variables to predict hospitalization. We use the 2012 national Medicaid Analytic eXtract data and Medicare Provider Analysis and Review data. Predicting any hospitalization, Random Forest appears to be the most robust approach, though XGBoost achieved similar predictive performance. While the importance of features varies by algorithm, chronic conditions, previous hospitalizations, as well as use of services for ambulance, personal care, and durable medical equipment were generally fo

SUBMITTER: Jung D 

PROVIDER: S-EPMC10069559 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature